Cyber Threat Detection Using Machine Learning Algorithms on Heterogeneous MiniVHS-22 Dataset
F M Arifur Rahman, Rafee Zunaied Tanna, Umme Habiba, Rizwan Shaikh, Zahidur Rahman, Hafiz Imtiaz · 2022
Large and varied amounts of data are needed for the research of emerging machine learning (ML) techniques for detecting network threats, such as malware-related threats. The research community has been using a number of network traffic datasets that have been proposed in recent years. The majority of these datasets contain, however, only a few classes of bot and malware, lacking significant diversity and generalization to identify threats. In this work, we considered a modified version of the VHS-22 dataset that we termed as MiniVHS-22. This dataset contains flow parameters extracted using a software network probe from four datasets and a network traffic malware monitoring website. Our methodology evaluates seven different machine learning techniques. More than 99% of the threats associated with malware are successfully identified by the Random Forest Classifier, Decision Tree, and Multilayer Perceptron. Additionally, we used different dimensionality reduction techniques such as the Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) with varying numbers of principal component values. Sophisticated network traffic threat detection systems can be developed using the results of our investigation.